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Greedy function approximation: A gradient boosting machine.
Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansionsExpand
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The Elements of Statistical Learning
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
TLDR
This book describes the important ideas in these areas in a common conceptual framework. Expand
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Regularization Paths for Generalized Linear Models via Coordinate Descent.
TLDR
We develop fast algorithms for estimation of generalized linear models with convex penalties. Expand
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Sparse inverse covariance estimation with the graphical lasso.
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--theExpand
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Stochastic gradient boosting
Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current "pseudo'-residuals by least squares at each iteration. TheExpand
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Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By
The main and important contribution of this paper is in establishing a connection between boosting, a newcomer to the statistics scene, and additive models. One of the main properties of boostingExpand
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Regularized Discriminant Analysis
Abstract Linear and quadratic discriminant analysis are considered in the small-sample, high-dimensional setting. Alternatives to the usual maximum likelihood (plug-in) estimates for the covarianceExpand
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Special Invited Paper-Additive logistic regression: A statistical view of boosting
Boosting is one of the most important recent developments in classification methodology. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training dataExpand
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PATHWISE COORDINATE OPTIMIZATION
We consider “one-at-a-time” coordinate-wise descent algorithms for a class of convex optimization problems. An algorithm of this kind has been proposed for the L1-penalized regression (lasso) in theExpand
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